On the relationship between sources of within- and between-group differences and measurement invariance in the common factor model

Investigating sources of within- and between-group differences and measurement invariance (MI) across groups is fundamental to any meaningful group comparison based on observed test scores. It is shown that by placing certain restrictions on the multigroup confirmatory factor model, it is possible to investigate the hypothesis that within- and between-group differences are due to the same factors. Moreover, the modeling approach clarifies that absence of measurement bias implies common sources of within- and between-group variation. It is shown how the influence of background variables can be incorporated in the model. The advantages of the modeling approach as compared with other commonly used methods for group comparisons is discussed and illustrated by means of an analysis of empirical data.     

The effect of priming a thin ideal on the subsequent perception of conceptually related body image words

Our research examined the effects of thin ideal priming on the perception of body image words in participants without an eating disorder. Half of the participants were primed by viewing thin models, and half were primed with gender-neutral shoes. Subsequently, all participants (N = 56) completed a Stroop task for three categories of words: neutral (BOOKS), shoe (CLOGS), and body (THIGHS). Lastly, all participants completed a body dissatisfaction questionnaire. We predicted that body dissatisfaction scores would be correlated with the Stroop effect. We found a significant correlation between body dissatisfaction and the body effect of slower color naming times for the body related words compared to the neutral words. Our study demonstrates that body dissatisfaction and a brief priming with thin models results in subsequent differences in performing a Stroop task in a non clinical population of female participants.     

Belief and aims

Does belief have an aim? According to the claim of exclusivity, non-truth-directed considerations cannot motivate belief within doxastic deliberation. This claim has been used to argue that, far from aiming at truth, belief is not aim-directed at all, because the regulation of belief fails to exhibit a kind of interaction among aims that is characteristic of ordinary aim-directed behaviour. The most prominent reply to this objection has been offered by Steglich-Petersen (Philos Stud 145:395?C405, 2009), who claims that exclusivity is in fact compatible with belief??s genuinely having an aim. I argue, based on consideration of what is involved in pursuing an aim, that Steglich-Petersen??s reply fails. I suggest that the defender of the idea that belief has an aim should instead reject the claim of exclusivity, and I sketch how this can be done.     

The Heteroscedastic Graded Response Model with a Skewed Latent Trait: Testing Statistical and Substantive Hypotheses Related to Skewed Item Category Functions

The Graded Response Model (GRM; Samejima, Estimation of ability using a response pattern of graded scores, Psychometric Monograph No.?17, Richmond, VA: The Psychometric Society, 1969) can be derived by assuming a linear regression of a continuous variable, Z, on the trait, ??, to underlie the ordinal item scores (Takane & de Leeuw in Psychometrika, 52:393?C408, 1987). Traditionally, a normal distribution is specified for Z implying homoscedastic error variances and a normally distributed ??. In this paper, we present the Heteroscedastic GRM with Skewed Latent Trait, which extends the traditional GRM by incorporation of heteroscedastic error variances and a skew-normal latent trait. An appealing property of the extended GRM is that it includes the traditional GRM as a special case. This enables specific tests on the normality assumption of Z. We show how violations of normality in Z can lead to asymmetrical category response functions. The ability to test this normality assumption is beneficial from both a statistical and substantive perspective. In a simulation study, we show the viability of the model and investigate the specificity of the effects. We apply the model to a dataset on affect and a dataset on alexithymia.     

What Assertion Doesn't Show

Abstract: Some recent arguments against the classical invariantist account of knowledge exploit the idea that there is a ‘knowledge norm’ for assertion. It is claimed that, given the existence of this norm, certain intuitions about assertability support contextualism, or contrastivism, over classical invariantism. In this paper I show that, even if we accept the existence of a knowledge norm, these assertability‐based arguments fail. The classical invariantist can accommodate and explain the relevant intuitions about assertability, in a way that retains the idea that knowledge is the epistemic norm for assertion. When we consider the role of assertion as a conversational act, it becomes plausible that a subject's epistemic warrant to assert can be defeated even though she has knowledge. This defeasibility thesis is what allows the classical invariantist to accommodate and explain the kinds of intuitions on which assertability‐based arguments depend.     

Reliability of testimonial norms in scientific communities

Several current debates in the epistemology of testimony are implicitly motivated by concerns about the reliability of rules for changing one’s beliefs in light of others’ claims. Call such rules testimonial norms (tns). To date, epistemologists have neither (i) characterized those features of communities that influence the reliability of tns, nor (ii) evaluated the reliability of tns as those features vary. These are the aims of this paper. I focus on scientific communities, where the transmission of highly specialized information is both ubiquitous and critically important. Employing a formal model of scientific inquiry, I argue that miscommunication and the “communicative structure” of science strongly influence the reliability of tns, where reliability is made precise in three ways.     

Marginal and Random Intercepts Models for Longitudinal Binary Data With Examples From Criminology

Two models for the analysis of longitudinal binary data are discussed: the marginal model and the random intercepts model. In contrast to the linear mixed model (LMM), the two models for binary data are not subsumed under a single hierarchical model. The marginal model provides group-level information whereas the random intercepts model provides individual-level information including information about heterogeneity of growth. It is shown how a type of numerical averaging can be used with the random intercepts model to obtain group-level information, thus approximating individual and marginal aspects of the LMM. The types of inferences associated with each model are illustrated with longitudinal criminal offending data based on N = 506 males followed over a 22-year period. Violent offending indexed by official records and self-report were analyzed, with the marginal model estimated using generalized estimating equations and the random intercepts model estimated using maximum likelihood. The results show that the numerical averaging based on the random intercepts can produce prediction curves almost identical to those obtained directly from the marginal model parameter estimates. The results provide a basis for contrasting the models and the estimation procedures and key features are discussed to aid in selecting a method for empirical analysis.     

A house divided? The psychology of red and blue America

Recently it has become commonplace in America for commentators and the public to use the terms "red" and "blue" to refer to perceived cultural differences in America and American politics. Although a political divide may exist in America today, these particular terms are inaccurate and reductive. This article presents research from social psychology demonstrating that the increased use of these terms is likely to increase the conflict between political groups in America by making political conflict salient in nonpolitical contexts, reducing the ability of Americans to form multifaceted complex identities, pushing Americans to misperceive political in-groups and out-groups, and contributing to a "spiral of silence." An alternative model for discussing cultural differences is proposed.     

A dynamical model of general intelligence: the positive manifold of intelligence by mutualism

Scores on cognitive tasks used in intelligence tests correlate positively with each other, that is, they display a positive manifold of correlations. The positive manifold is often explained by positing a dominant latent variable, the g factor, associated with a single quantitative cognitive or biological process or capacity. In this article, a new explanation of the positive manifold based on a dynamical model is proposed, in which reciprocal causation or mutualism plays a central role. It is shown that the positive manifold emerges purely by positive beneficial interactions between cognitive processes during development. A single underlying g factor plays no role in the model. The model offers explanations of important findings in intelligence research, such as the hierarchical factor structure of intelligence, the low predictability of intelligence from early childhood performance, the integration/differentiation effect, the increase in heritability of g, and the Jensen effect, and is consistent with current explanations of the Flynn effect.     

The effectiveness of drug treatment programs in reducing criminal behavior: a meta-analysis

The main aim of this paper is to assess the effectiveness of drug treatment programs in reducing criminal behavior. The research is based on a systematic review and meta-analysis of evaluations of drug treatment programs. The research investigated various drug treatment programs including methadone treatment, therapeutic communities, post-release supervision for drug-misusing offenders, and drug courts, and focused on drug treatment programs that were primarily concerned with heroin, crack and cocaine misuse. The main outcome variable analyzed was reoffending following treatment as measured by self-reports or official records. The study was based on a meta-analysis of 28 evaluations of drug treatment programs. The meta-analysis involved calculating individual effect sizes for each study and weighted mean effect sizes for groups of studies. The mean odds of offending following treatment were significantly lower among clients of the drug treatment programs than among the comparison groups. However, the results varied by type of program, type of evaluation methods used, and characteristics of clients.